Prediction of Small-Molecule Partitioning into Biomolecular Condensates from Simulation

Abstract

Predicting small-molecule partitioning into biomolecular condensates is the key to developing drugs that selectively target aberrant condensates. However, the molecular mechanisms underlying small-molecule partitioning remain largely unknown. Here, we first exploit atomistic molecular dynamics simulations of model condensates to elucidate the physicochemical rules governing small-molecule partitioning. We find that while hydrophobicity is a key factor in determining partitioning into condensates enriched in hydrophobic residues, partitioning into more polar condensates is driven by specific interactions that can offset the associated entropic cost of localization. The observed selectivity of condensates toward certain compounds suggests that condensate-specific therapeutics can be engineered. Building on these insights, we develop minimal models (MAPPS) for the efficient prediction of small-molecule partitioning into biologically relevant condensates. We demonstrate that this approach reproduces atomistic partition coefficients in both model systems and condensates composed of the low-complexity domain (LCD) of FUS. Applying MAPPS to various LCD-based condensates shows that the protein sequence can exert a selective pressure, thereby influencing small-molecule partitioning. Collectively, our findings reveal that partitioning is driven by both small molecule-protein affinity and the complex interplay between the physicochemical properties of the compounds and the condensate environment.

Keywords: biomolecular condensates; coarse-grained model; molecular dynamics; partitioning; small molecules.

1

Atomistic molecular dynamics simulations are used to probe the partitioning of organic molecules into model condensates. (a) Chemical structures of the simulated compounds. Colored circles in the titles correspond to the colors of the markers for the data points reported throughout the work. The molecules are ordered alphabetically. (b) Snapshot from the all-atom molecular dynamics simulations. Part of the simulation box is shown, illustrating the partitioning of styrene into an FYAFYF model condensate (protein is colored in pink colors, small molecules are in yellow, water is in blue, and salt ions are in green and gray).

2

Physicochemical properties of small molecules, as well as the chemical environment of condensates, dictate partitioning. Partitioning coefficient of a set of small molecules into model condensates composed of NYANYN (first row), YQHQHY (second row), FYAFYF (third row), and FWAFWF (fourth row) peptides, based on all-atom simulations at 300 K, plotted against the compounds’ (a) log P values (partition coefficient between octanol and water), (b) solubility (log₁₀) at 25 °C, and (c) molecular diameter. The solid black line shows a linear fit with a corresponding Pearson correlation coefficient. Error bars represent standard errors. The concentration of the small molecules in each system was 100 mM, and the protein concentration was 300 mM for FYAFYF, NYANYN, and YQHQHY peptides and 240 mM for FWAFWF peptides.

3

Illustration of the parametrization algorithm used to develop MAPPS. Results for 2-methylpropane are shown as an example. (a) The potential of mean force for molecule self-interaction was used to determine the molecule self-interaction parameters of the Wang–Frenkel potential. (b) A series of slab simulations with Y₆ peptides at 300 K and a salt concentration of 150 mM in coarse-grained resolution was used to optimize the molecule–tyrosine interaction parameter, ε_SM–Y ^fitted, to reproduce partitioning behavior observed in the all-atom simulations of the corresponding system. (c) The distribution of the normalized contact frequencies between the molecule and a single protein chain, obtained from all-atom simulations at 300 K and a salt concentration of 150 mM, was used to derive interaction parameters for the molecule with other residues (ε_SM–X). (d) Validation of the parameterization based on the brute force optimization of SM–X interactions via all-atom slab simulations of (YXY)₂ peptides for each residue (i.e., ε_SM–Y ^fitted) vs ε_SM–X values obtained through eq . (e) Snapshot from a coarse-grained molecular dynamics simulation illustrating the partitioning of the molecules into an LCD condensate, simulated with parameters obtained via steps 1–4. Note that only part of the simulation box is shown.

4

Validation of transferability of MAPPS using model peptide-based condensates. Coarse-grained (CG; using MAPPS) and all-atom (AA) simulations of a condensate with small molecules were conducted at 300 K and 150 mM salt concentration, with the protein concentration of 300 mM for FYAFYF, NYANYN, and YQHQHY peptides, a protein concentration of 240 mM for FWAFWF peptides, and the small molecule concentration of 100 mM. (a) Partitioning coefficients of a subset of small molecules (cisplatin, phenol, 2-methylpropane, styrene, and nitrobenzene) in the CG simulation versus AA simulations for a set of peptides of various degrees of polarity (FWAFWF, NFWAFS, YQHQHY, FYAFYF, and NYANYN). Partitioning of a larger set of small molecules in the CG simulation versus AA simulation for (b) FYAFYF and FWAFWF peptides and (c) YQHQHY and NYANYN peptides. The Pearson correlation coefficient r and the root-mean-square deviation (RMSD) values are displayed. The dashed line represents the equity line.

5

Simulations of the FUS LCD condensate with small molecules in all-atom (AA) and coarse-grained (CG) resolutions. Simulations were conducted at 300 K and 150 mM salt concentration. 64 chains of FUS LCD were simulated at a protein concentration of 12.5 mM and small molecule concentration of 30 mM. (a) Backmapping procedure to obtain the AA configuration of the condensed FUS LCD chains. (b) Partitioning coefficients obtained in the CG simulation versus AA simulation. The error bars for the values from the CG simulations are within the markers. (c) Normalized contact frequency between a molecule and a single residue of FUS-LCD in the CG simulation versus AA simulation (each marker corresponds to a single residue). The errors are computed as standard errors for the frequency of contacts across the analyzed time frames.

6

Coarse-grained simulations of small-molecule partitioning into condensates of low-complexity domains of proteins. The low-complexity domains of the following proteins were simulated: TIA1, EWSR1, FUS, and A1 at 20, 7, 12, and 16 mM concentrations, respectively. The concentration of the small molecules for each system was 30 mM. Simulations were conducted at 300 K and 150 mM salt concentration. (a) Compositions of the residues for the simulated LCDs. The following residues classification is used: “aliphatic” = A, I, L, M, V; “aromatic” = F, W, Y; “polar” = N, Q, S, T; “charged (+)” = R, K, H; “charged (−)” = D, E; “other” = C, G, P. (b) Partitioning coefficients K for TIA1, EWSR1, FUS, and A1 LCDs per compound. Error bars represent standard errors. The inset plot is the box-and-whisker plot of partitioning coefficients K for each protein across all tested molecules. The box represents the interquartile range (IQR), with the lower and upper edges corresponding to the first (Q1) and third (Q3) quartiles, respectively. The horizontal line inside the box indicates the median K value. (c–f) Partitioning coefficients plotted against the compounds’ log P values. The yellow arrow represents the decreasing percentage of the polar residues among the LCDs. Displayed value r is the Pearson correlation coefficient.